Sold by: Pinecone
Deployed on AWS
Only for accepting private offers. Pinecone is a serverless vector database built to power production AI on AWS. It delivers fast, accurate retrieval with hybrid search, reranking, filtering, and real-time indexing - no infrastructure or tuning required. Purpose-built for scale, Pinecone handles billions of vectors with low latency and high reliability. Teams use Pinecone to power agents, semantic search, recommendations, and RAG pipelines without managing infrastructure or stitching together open-source tooling. With fully managed operations and predictable performance, developers can focus on building intelligent applications instead of operating vector infrastructure.
4.4
Overview
Video
Pinecone Demo
Video
Getting started on AWS
Pinecone's fully managed, serverless vector database makes it easy to build accurate AI applications in production. By combining hybrid search (semantic + keyword), integrated reranking, hosted embedding and inference models, and real-time indexing, Pinecone delivers fast, relevant results at any scale, from prototype to billions of vectors.
Vector workloads aren't one-size-fits-all. From bursty RAG pipelines to high-throughput, latency-sensitive search and recommendation systems, Pinecone supports a full range of production use cases on a single platform.
- On-Demand provides elastic, usage-based scaling for variable traffic
- Dedicated Read Nodes (DRN) provide provisioned read capacity for predictable latency and sustained throughput .
Together, On-Demand and DRN let you optimize price-performance for each workload without managing multiple systems.
Pinecone integrates deeply with the AWS ecosystem, including services like Amazon Bedrock and SageMaker, while also supporting the most popular AI frameworks and data platforms. Developers use Pinecone to power agents, semantic search, recommendations, and RAG pipelines through a simple, intuitive API.
No infrastructure to manage, no algorithms to tune - just the performance, security, and reliability production AI demands.
Billing
This listing is intended for customers purchasing Pinecone through a private offer with an annual commitment. Annual commitments provide volume-based pricing and additional commercial benefits based on your usage level.
To get started, please contact your Pinecone sales representative or visit https://www.pinecone.io/contact/ to discuss custom pricing and terms before subscribing through this page.
If you prefer to start without an annual commitment, use Pinecone's Pay As You Go product listing.
Note: The "Pinecone Billing Unit" displayed below is an AWS Marketplace requirement and does not reflect Pinecone's actual pricing model or metering.
Highlights
- Accurate, production-ready retrieval: Pinecone delivers low-latency search (20-100ms) on billion-vector datasets with hybrid search (semantic + keyword), integrated reranking, and real-time indexing. Built on a purpose-built Rust engine and serverless architecture, optimized for production AI, not just vector storage.
- Ship faster with predictable cost and scale: Go from prototype to production in days, not months. Fully managed serverless architecture with decoupled storage and compute and no infrastructure to manage. Scales from thousands to billions of vectors with On-Demand or Dedicated Read Nodes and a 99.9% uptime SLA.
- Enterprise-ready with a rich ecosystem: SOC 2 Type II and HIPAA certified with security enforced at the data layer. 50+ integrations with the most popular AI and data tools, including deep support across the AWS ecosystem.
Details
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
Buyer guide
Gain valuable insights from real users who purchased this product, powered by PeerSpot.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
Commit | Total Commitment Value | $100,000.00 |
Dimensions summary
This listing uses a single pricing dimension: a Total Commitment Value billed as an annual contract. You agree to a set spending amount upfront for the term. Your actual usage of the vector database draws down against that commitment. Usage covers activity like storage, write and read operations, and related services, billed by the units defined for each. Larger commitment amounts unlock associated discounts and support benefits. Because pricing is committed rather than pay-as-you-go per unit, you get predictable annual spend while retaining flexibility in how you consume the underlying services.
Top-of-mind questions for buyers
Your commitment covers vector database activity billed by defined units. This includes storage per gigabyte, write and read units, backup and restore per gigabyte, and object storage imports. Inference services like embedding and reranking, plus assistant token usage, also draw down. All metered usage counts against the committed amount.
It depends on your workload. Storage charges per gigabyte per month dominate for large, low-traffic datasets. Write and read units drive cost for high-throughput or high-query applications. Inference and assistant token usage add on top. All these charges accrue simultaneously and draw from the same committed amount.
The commitment is a set spending amount you agree to upfront. Once your metered usage draws down the full committed value, additional usage is billed separately. To adjust your committed spending, you contact the vendor directly, since annual commitments are arranged with their team.
www.pinecone.io+1
Helpful?
Vendor refund policy
Please contact us at support@pinecone.io
Custom pricing options
Request a private offer to receive a custom quote.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
This is a fully managed service with technical support included with Standard and Enterprise plans. For more information regarding support SLAs, please see each plan's details on the pricing page. support@pinecone.io support@pinecone.io
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Pinecone
By Qdrant
By Weaviate
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Hybrid Search Capabilities
Combines semantic and keyword search with integrated reranking and real-time indexing for accurate retrieval across billion-vector datasets
Low-Latency Performance
Delivers search latency of 20-100ms on billion-vector datasets built on a purpose-built Rust engine and serverless architecture
Scalable Architecture
Supports elastic scaling from thousands to billions of vectors through On-Demand usage-based scaling and Dedicated Read Nodes for provisioned read capacity
Security and Compliance
SOC 2 Type II and HIPAA certified with security enforced at the data layer
Ecosystem Integration
Provides 50+ integrations with popular AI and data tools including deep support for Amazon Bedrock, SageMaker, and major AI frameworks
Vector Search Engine
High-performance vector search engine for storing, searching, and managing vector embeddings with production-ready service capabilities
Advanced Filtering Support
Extended filtering capabilities on additional metadata fields that can be stored as payload along with vector embeddings
Flexible Storage Options
Multiple storage configuration options to support various deployment and scalability requirements
API Interface
Convenient API for storing, searching, and managing vectors with payload support
Unstructured Data Processing
Support for neural network encoders and embeddings to enable matching, searching, and recommendation applications on unstructured data
Vector Similarity Search
End-to-end vector database supporting vector similarity search, hybrid search, and advanced filtered search capabilities.
Multimodal Data Support
Out-of-the-box support for multimodal media types including text, images, and other data formats.
Structured Filtering
Ability to seamlessly combine vector search with structured filtering for refined query results.
Cloud-Native Architecture
Fault-tolerant cloud-native database architecture with low-latency performance characteristics.
Multi-Language Client Support
Accessible through a variety of client-side programming languages for flexible integration.
Standard contract
No
No
No
Customer reviews
Akash R.
Fast, Scalable Vector Search Perfect for AI Applications
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
I appreciate how quickly Pinecone can search through large amounts of vector data and return relevant results, which makes building AI applications easier since I don't have to manage the underlying vector search infrastructure. I also value Pinecone's vector database, similarity search, and metadata filtering features. These make it simple to store embeddings, quickly retrieve relevant information, and narrow results based on metadata, which is very useful for RAG applications. Additionally, the initial setup of Pinecone was fairly straightforward, allowing me to connect it to our application and create the index with minimal time required. This ease of use, combined with the fast and scalable vector search capabilities, and the ability to handle larger datasets, has been quite beneficial.
What do you dislike about the product?
One area that could be improved is the learning curve when setting up and optimizing indexes, especially for someone new to vector databases. I'd also like more straightforward guidance around tuning search performance and managing costs as the amount of data and query volume increases. For search performance, better recommendations around index configuration, metadata filtering, and retrieval settings would be helpful, especially for larger datasets. On the cost side, clearer usage estimates and alerts for high query volume or storage growth would make it easier to monitor spending and optimize resources before costs increase unexpectedly.
What problems is the product solving and how is that benefiting you?
Pinecone solves the problem of quickly finding relevant information from large unstructured data by making vector search faster and scalable. It helps with RAG applications by providing accurate context for AI models and eliminating the need to manage search infrastructure myself.
Vikash K.
Stress-free embedding storage and fast vector search without infrastructure overhead, Pinecone is gold.
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
As an AI-engineer, we used multiple vector databases, but for our claim processing agent, we were looking for something where a small embedding data set would not make a headache of infrastructure issues and while adjuster uploading multi-page claim file chunk and embed each line item description should be seamless. We also found the Pinecone algorithm for indexing is far better than ScaNN or DiskANN. No latency and a smart caching layer help a lot in a smoother RAG pipeline.
What do you dislike about the product?
From a devOps side, we can't extract raw vectors completely and rebuild with another database.
What problems is the product solving and how is that benefiting you?
In our project, we are helping adjusters to reduce manual review and here semantic retrieval for our multiple agents Pinecone working like a charm. As our agents do embedding, categorisation, pricing and depreciation at all pipeline levels, we are taking help for overall claim-pricing accuracy.
Prashant V.
Straightforward Vector Search for Fast, Reliable RAG Retrieval
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
Pinecone has been useful for handling vector search without adding too much complexity to the application. I found the indexing and similarity search fairly straightforward, and metadata filtering is also useful when we need more relevant results. It works particularly well for RAG use cases where fast retrieval of the right information is important.
What do you dislike about the product?
The initial setup is not too difficult, but understanding the right index configuration and embedding setup takes some time. Cost can also become a concern when the data and query volume increases. More visibility into cost estimation and usage would make it easier to plan for larger workloads.
What problems is the product solving and how is that benefiting you?
Earlier, managing vector search and finding the right data for RAG applications required more effort on the application side. With Pinecone, we can store embeddings and quickly retrieve the most relevant results using similarity search and metadata filters. This reduces the search-related development work and helps improve the response quality of AI applications.
Anson D.
Clean Interface and Easy Vector Search Setup with Pinecone
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
Pinecone makes it easy to store and search vector data for AI and semantic search use cases. The interface is clean, and setting up indexes and managing vector data is straightforward. The documentation is also helpful when getting started.
What do you dislike about the product?
There are several concepts around indexes, embeddings, and vector search that can take some time to understand for beginners. Some advanced features may also require additional learning.
What problems is the product solving and how is that benefiting you?
Pinecone helps simplify the storage and retrieval of vector data for AI applications. It makes semantic search and retrieval easier to implement without having to build and maintain the entire vector-search infrastructure ourselves.
George P.
Simple and Effective Vector Search for AI Applications
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
I like how easy Pinecone makes it to add vector search to an application. The API is straightforward, and I can quickly store embeddings and retrieve relevant results without having to manage the database infrastructure myself. It has been especially useful when working on AI features that need fast and relevant information retrieval.
What do you dislike about the product?
The main thing I would improve is the learning curve when setting up some of the more advanced configurations. It can take some time to understand the different index and search options, especially when deciding which setup is best for a particular application. More guidance around those choices would make the experience easier for new users.
What problems is the product solving and how is that benefiting you?
Pinecone helps me handle vector data and semantic search without building the entire retrieval layer from scratch. This makes it easier to connect AI applications to relevant data and quickly retrieve information based on meaning rather than only exact keywords. It saves development time and lets me focus more on the application itself.